Project Summary (maximum 30 lines) The BRAIN initiative seeks to develop and apply technologies in order to understand of how brain cells interact in both time and space to give rise to brain function. A key deliverable in BRAIN is a systematic census of neuronal and glial cell types, which is a prerequisite to understand how these cells interact and change in healthy aging and in disease. Moreover, such a census will provide a common reference cell taxonomy, which is crucial to harmonize studies at different sites and achieve the goals of BRAIN. A necessary companion of the census is a reference coordinate system, which enables us to understand the spatio-anatomical context in which cells interact, as well as their connectivity. Building such a coordinate system requires advanced spatial alignment (registration) tools, since virtually every lab technique used in microscopic brain cell phenotyping ? particularly in human brain ? requires blocking and/or sectioning of samples, hence distorting the structure of tissue. Due to the difficulty of providing support for datasets and acquisition setups different to the original, most publicly available techniques to recover the lost tissue structure (?3D reconstruction?) rely on very simple techniques, such as vanilla pairwise registration of neighboring sections. Moreover, conventional reconstruction methods are notoriously slow, and no available method is designed to 3D reconstruct whole human brains. In this interdisciplinary project, which lies at the nexus of computer science, MRI physics, histology, optical imaging, anatomy and statistics, we propose to extend, robustify, test, distribute and support our recently developed, state-or-the-art techniques that will enable the constructions of a coordinate system capable of representing multi-scale maps of human brain anatomy and function. This includes algorithms and software for: image analysis of ex vivo MRI; construction of laminar models of the human cerebral cortex; 3D reconstruction of microscopic images and alignment to the laminar models; surface based analysis of microscopy data on the laminar structure; and alignment of ex vivo and in vivo images to accurately transfer information from microscopy to MRI studies of the living brain, in health and in disease. The tools we propose to build and disseminate will combine modern deep learning techniques with principled Bayesian inference, and have the potential to deliver accurate registration at the macroscopic, mesoscopic, and microscopic level, with high throughput delivered using cutting-edge machine learning algorithms. Effective dissemination of these tools, along with companion test data, will be achieved through our widespread package FreeSurfer. The distributed tools will not only enable the construction of a cell census with rich spatial information at human brain scale (including a novel laminar model), but will also have a tremendous impact in other areas of neuroimaging, including overarching goals of BRAIN such as: linking cellular-level activity to functional MRI, atlas building, or connecting axonal anatomy to diffusion MRI.